Dexterous manipulation using an under-actuated hand has been a challenging task due to its non-linear dynamical characteristics. For a linkage-based under-actuated hand designed to be used to grasp and manipulate large, heavy, and rigid objects stably, precision grasping is necessary, which makes the task even more difficult to deal with. While approaches based on external sensors have been introduced throughout the years, to create a robotic hand that can be used for various tasks in unstructured environments, this paper takes the standpoint that control techniques that do not fully depend on utilizing additional sensing elements need to be further developed. This paper applies the hybrid method using analytics models and data-driven-based approaches to analyze internal sensors' data during the operation of the robot and introduces novel data-driven-based techniques to compensate for the limitations of controlling a linkage-based under-actuated hand with a self-locking mechanism. Then, a within-hand object position manipulation framework with proposed methodologies is presented and experimented with to show its effectiveness.

This article proposes novel control methods that lower impact force by preemptive movement and smooth transition to conventional contact-based impedance control. These techniques are suggested for application in force-control-based robots and position/velocity-control-based robots. Strong impact forces have a negative influence on multiple robotic tasks. Recently, preemptive impact reduction techniques that expand conventional contact impedance control using proximity sensors have been examined. However, a seamless transition from impact reduction to contact impedance control has yet to be demonstrated. It has, therefore, been necessary to switch control strategies or perform complicated parameter tuning. In contrast, our proposed methods utilize a serial combined impedance control framework to solve these problems. The preemptive impact reduction feature can be added to an already-implemented impedance controller because the parameter design is divided into impact reduction and contact impedance control. There is no discontinuity or abrupt alteration in the contact force, nor are there any excessively large contact forces that exceed the intended repulsive force established by the contact impedance control during the transition. Furthermore, although the preemptive impact reduction uses a crude optical proximity sensor, the influence of reflectance is minimized by employing a virtual viscous force. Analyses and real-world experiments with a 1-D mass model confirm these features, which are useful for many robots performing contact tasks.

This study focuses on replicating the muscu-loskeletal system of human arms for mimicking its movement. Muscle redundancy is critical for regulating the mechanical impedance of arms and legs. However, when implementing muscle redundancy on robots, making an ill-posed problem that cannot determine the muscle forces uniquely. In this paper, first, a method for controlling end-point stiffness in the muscle space for the joint and muscle redundant system is described. Next, the muscle model imitating the nonlinear viscosity characteristic of human muscles is introduced. Then, a method to control the joint viscosity by adjusting the internal forces of muscles adequately without affecting the stiffness control directly is proposed. Finally, numerical simulations are performed to investigate the effectiveness of the proposed method.

This paper proposes a novel force controller that serially combines admittance and impedance controllers. The proposed controller is adaptable to an unknown changeable environment in terms of stiffness, and it is able to achieve high control accuracy and stable operation. First, conventional admittance and impedance controllers are recalled, and based on them, a new force controller is designed. Next, the proposed controller is applied to a one DoF system in contact with an external environment in the case where the contact stiffness is changeable, and compare the behavior of the proposed controller with that of the conventional simple admittance and impedance controllers through numerical simulations. Additionally, the proposed controller is applied to a two DoFs system including some nonlinearities, and proposes a design of the desired anisotropic admittance and impedance parameters to the proposed controller. This effectiveness is also demonstrated through numerical simulation results.

In this paper, our previously proposed set-point control method for a musculoskeletal system is improved to reduce required muscular forces and to avoid a saturation of muscular forces during movement. The previous method is robust against a considerable time-delay in sensory information, but it still requires large muscular forces to accomplish a desired position, and the maximum exertable muscular force has not yet been taken into consideration. To cope with these two issues, two variable parameters are newly introduced. One is for changing the combination ratio of feed-forward and feedback controllers to reduce necessary muscular forces. The other is for avoiding the saturation of muscular forces during movement The effectiveness of the proposed controller is demonstrated through several numerical simulation results.

This paper considers experimental investigation of an antagonistically actuated robot manipulator. The focus of the investigation is how biarticular actuation contributes to control of the manipulator. Based on the feature that antagonistically robot manipulators can be controlled by feedforward control with constant inputs, this paper treats the case of PTP (point-to-point) control of robot postures. Due to expectation of difficulty for analytical approach, this paper takes experimental methodology of acquiring mappings between sensory and motor spaces by ANNs (artificial neural networks). Based on the idea that contribution of biarticular actuation may be influenced by choice of a sensory space, this paper considers not only joint angles of a manipulator but also Cartesian coordinate and angles in binocular visual space at the hand of the manipulator as sensory spaces. For each of them, the mappings obtained from trained ANNs are compared for the cases with and without biarticular actuation, based on interpolation performance of the ANNs examined through PTP control of the manipulator. The results of the comparison shows that choice of a sensory space is less effective to the interpolation performance in the case of biarticular actuation.

The human body possesses a musculoskeletal structure in which muscles exist around the bones and joints. The musculoskeletal tendon-driven robot utilizes this structure. This robotic system uses sets of mechanical tendons, such as wire-cables and actuators instead of the vital muscles. The redundant actuation is necessary for the system when it does not actively use any external force nor a tensioner because the mechanical tendon can transmit only a tensile force. This structural characteristic enables feedforward motion-generation that does not need any sensory feedback. However, the convergent posture strongly depends on the tendon-arrangement. Targeting the tendon-driven manipulator, which has two links and six tendons, this paper expands the mathematical conditions for the convergence into the geometric conditions of tendon-arrangement. Based on the geometric conditions, a design method of the tendon-arrangement is discussed.

We propose an evaluation method of grasp stability which takes into account the elastic deformation of fingertips from the viewpoint of energy. An evaluation value of grasp stability is derived as the minimum energy which causes slippage of a fingertip on its contact surface. To formulate the evaluation value, the elastic potential energy of fingertips and the gravitational potential energy of a grasped object are considered. It is ensured that fingertips do not slip on grasped object surfaces if the external energy applied to the object is less than the evaluation value. Since our evaluation value explicitly considers the deformation values of fingertips, grasp stability is evaluated by taking into consideration the contact forces generated by the deformation. The effectiveness of our method is verified through numerical examples.

Object-level impedance control is of great importance for object-centric tasks, such as robust grasping and dexterous manipulation. Despite the recent progress on this topic, how to specify the desired object impedance for a given task remains an open issue. In this paper, we decompose the object's impedance into two complementary components-the impedance for stable grasping and impedance for object manipulation. Then, we present a method to learn the desired object's manipulation impedance (stiffness) using data obtained from human demonstration. The approach is validated in two tasks, for robust grasping of a wine glass and for inserting a bulb, using the 16 degrees of freedom Allegro Hand mounted with the SynTouch tactile sensors.

This paper proposes a novel set-point control method of a musculoskeletal system by combining a feedforward and feedback manner to complement each drawback each other. In our previous work, a feedforward positioning method of the musculoskeletal arm model was proposed which does not need any realtime sensory information. Its performance, however, depends on a muscular arrangement and an attitude of the arm, and thereby a large initial muscular internal force is necessary to make a good performance. On the other hand, it is well-known that a visual servoing is effective and versatile for the set-point control. However, there is a considerable time-delay due to a computational burden to acquire useful information from an image and an insufficient sampling period to capture each image when using a video frame rate camera. Thus in this paper, the feedforward and feedback signal are linearly combined into one in order to mutually complement each drawback. The combined control signal is newly designed and then numerical simulation results are shown to demonstrate the effectiveness and usefulness of the proposed method.

This paper presents an improvement of our previously proposed dynamic object grasping and manipulation method. This method, which utilizes a dual-fingered hand of which each soft and hemispheric fingertip owns a torsional joint, has performed novel 3-dimensional dynamic object grasping and manipulation stably without the use of any external sensing device. However, several limitation and weakness have still remained in terms of dexterity and robustness. In order to improve it, firstly a triple-fingered hand is newly introduced instead of the dual-fingered hand to perform regrasping. Next, the previously proposed stable object grasping controller is adequately modified to perform dynamic regrasping during manipulation. Additionally, the virtual object attitude controller is also modified to improve its performance. Finally, several experiments are conducted by using a prototype, and the usefulness of proposed controller is demonstrated through these results.

This paper proposes a new visual servoing method for object manipulation robust to considerable time-delays of visual information. There still remain several problems in visual servoing methods although they are quite useful and effective for dexterous object manipulation. For instance, time-delays to obtain necessary information for object manipulation from visual images induce unstable behavior. The time-delays are mainly caused by low sampling rate of visual sensing system, computational cost for image processing, and latency of data transmission from visual sensor to processor. The method makes it possible to avoid such unstable behavior of the systems due to considerable time-delays using virtual object frame defined by only each joint angle. Firstly, a new control scheme for object manipulation using the virtual object frame is designed. Next, numerical simulations are conducted to verify the effectiveness of the control scheme. Finally, experimental results are shown to demonstrate the practical usefulness of proposed method.

In this paper, a new iterative learning control method which uses multiple space variables for a musculoskeletal-like arm system is proposed to improve the robustness against noises being included in sensory information. In our previous works, the iterative learning control method for the redundant musculoskeletal arm to acquire a desired endpoint trajectory simultaneous with an adequate internal force was proposed. The controller was designed using only muscle space variables, such as a muscle length and contractile velocity. It is known that the movement of the musculoskeletal system can be expressed in a hierarchical three-layered space which is composed of the muscle space, the joint space and the task space. Thus, the new iterative learning control input is composed of multiple space variables to improve its performance and robustness. Numerical simulations are conducted and their result is evaluated from the viewpoint of the robustness to noises of sensory information. An experiment is performed using a prototype of musculoskeletal-like manipulator, and the practical usefulness of the proposed method is demonstrated through the result.

This paper proposes an iterative learning control scheme for a redundant manipulator to acquire a skilled hand writing motion of its end-point specified on an arbitrary smooth surface. Firstly, the existence of a unique solution to the Lagrange equation of motion of the robot, whose end-point motion is coincident with a given desired end-point trajectory described in Cartesian coordinate system, is shown theoretically. Second, the iterative learning control signal that enables the robot end-point to trace a desired trajectory specified on an arbitrary smooth surface with fulfilling a desired contact force is designed. Next, a numerical simulation for the iterative learning scheme is conducted to show the effectiveness of the proposed controller, and its result is compared to a theoretically derived desired joint angle trajectory. This comparison shows that there exists a unique solution of the desired joint angle trajectory when an initial pose of the manipulator and a desired end-point trajectory on the constraint surface are given, even under the existence of holonomic constraint and joint redundancy.

This paper proposes a novel vision-based grasping and manipulation scheme of a multi-fingered hand-arm system robust for a temporary lack of sensory information. Visual information is one of the fundamental components for reliable grasping and manipulation by a multi-fingered hand-arm system. However, in case that visual information such as position and attitude of an object comes to be unavailable due to the occlusion or if the object goes out-of-sight temporarily, unstable and unfavorable behavior is often induced. The proposed method, which utilizes the stable grasping control and the concept of virtual frame, enables to grasp and manipulate an object stably even if the visual information becomes suddenly and temporarily unavailable during manipulation. Firstly, a dynamical model of object grasping using a multi-fingered hand-arm system is formulated. Next, a new control scheme for robust object grasping and manipulation using the virtual frame is proposed. Finally, numerical simulations are performed to verify the usefulness of the proposed method.

This paper proposes a novel high-backdrivable parallel-link manipulator with Continuously Variable Transmission (CVT) mechanism using several shaft actuators. The backdrivability is important and necessary for robots that operate around our life space to guarantee the safety of us, themselves and environments. A rotational actuator with a high reduction ratio reducer has been commonly used in traditional robotics field, but it cannot realize the high-backdrivability without any sensory feedback. In this study, several high-backdrivable shaft actuators are used instead of it. Moreover, the CVT mechanism which is brought by a novel configuration of the parallel mechanism is employed effectively. To utilize multiple shaft actuators with the CVT mechanism is able to surmount a drawback of the shaft actuator whose output force is relatively weak. A simple 1 DOF manipulator is proposed as the first step of this study. Firstly, its kinematic and dynamic models are given. Next, a PD type feedback control signal to regulate the arm's angle and the CVT simultaneously is designed. A static relation between an output end-point force and the CVT mechanism is analyzed. Finally, several fundamental experiments are conducted to show the effectiveness of proposed mechanism.

This paper proposes a novel object manipulation method to regulate the position and attitude of an object in the task space with dynamic stability by using a triple soft-fingered robotic hand system. In our previous works, a dynamic object grasping method without use of any external sensing, called “the Blind Grasping”, has been proposed. Although stable grasping in a dynamic sense has been realized by the method, a simultaneous object position and attitude control has not yet been treated, so far. In this paper, instead of using any information of the real object position and attitude, virtual data of object position and attitude are introduced by defining a virtual frame. By using the virtual information, a control signal to regulate the virtual object position and attitude without use of any external sensing is designed. The usefulness of our proposed control method even under the existence of nonholonomic rolling constraints is illustrated through a numerical simulation result.

This paper proposes a novel object manipulation method by using a dual soft-fingered robotic hand system, in which each fingertip has a torsional joint. By using the torsional motion of the fingertips, a novel 3-dimensional dynamic object manipulation without use of any external sensing can be achieved even though the hand system has only two fingers. Firstly, our proposed system, which includes contact models between each fingertip and surfaces of an object, is modeled. A rolling contact between each fingertip and the object surfaces can be allowed because our proposed system has soft and deformable hemispheric fingertips. Moreover, a torsional contact model between each fingertip and the object surfaces is newly proposed. It is based on an assumption that the torsional motion induces an elastic strain potential. Secondly, a dynamic object manipulation control method is designed. The control signal is composed of four parts, the one is for grasping the object stably, the second one is for controlling a position of the object, the third one is for controlling an attitude of the object, and the last one is for the torsional fingertip motion. A numerical simulation based on our model is performed, and a manipulation experiment by using our developed setup is performed. The usefulness of our proposed method is demonstrated through these results.

This paper describes a tactile sensing to estimate the position and orientation of a joint-axis of a linked object. This tactile sensing is useful when a multi-jointed multi-fingered robotic hand manipulates a tool which has a joint. This estimation requires sensing of the location of a contact point and the direction of an edge of the tool as contact information measured by a robotic fingertip. A conventional hard fingertip with a force sensor can measure only the location of a contact point. In contrast, we have already developed a robotic fingertip with a force sensor and a soft skin, and it can measure not only the location of a contact point but also the direction of an edge of an object. The estimation of a joint-axis of a linked object is demonstrated by using the soft fingertip.

This paper presents a proposal of an iterative learning control method for a musculoskeletal arm to acquire adequate internal force to realize human-like natural movements. Additionally, a dynamic damping ellipsoid at the end-point is introduced to evaluate internal forces obtained through the iterative learning. In our previous works, we have presented that a human-like smooth reaching movement using a musculoskeletal redundant arm model can be obtained by introducing a nonlinear muscle model and “the Virtual spring-damper hypothesis”. However, the internal forces have been determined heuristically, so far. In this paper, an iterative learning control method is used for acquisition of an adequate dynamic damping ellipsoid according to a given task, in order to determine internal forces more systematically. It is presented that the learning control scheme can perform effectively to realize given desired tasks, even under the existence of strong nonlinear characteristics of the muscles. After acquiring a given task, the dynamic damping ellipsoid is introduced to evaluate the relation between a damping effect generated by the acquired internal forces and a trajectory of the end-point. Some numerical simulations are performed and the usefulness of the learning control strategy, despite strong nonlinearity of the muscles, is demonstrated through these results.

A Riemannian-geometry approach for control and stabilization of dynamics of object manipulation under holonomic or non-holonomic (but Pfaffian) constraints is presented. First, position/force hybrid control of an endeffector of a multi-joint redundant (or nonredundant) robot under a nonholonomic constraint is reinterpreted in terms of ldquosubmersionrdquo in Riemannian geometry. A force control signal constructed in the image space spanned from the constraint gradient can be regarded as a lifting in the direction orthogonal to the kernel space. By means of the Riemannian distance on the constraint submanifold, stability on a manifold for a redundant system under holonomic constraints is discussed. Second, control and stabilization of dynamics of two-dimensional object grasping and manipulation by using a pair of multi-joint robot fingers are tackled, when a rigid object is given with arbitrary shape. Then, it is shown that rolling contact constraint induce the Euler equation of motion in an implicit function form, in which constraint forces appear as wrench vectors affecting on the object. The Riemannian metric can be introduced in a natural way on a constraint submanifold induced by rolling contacts. A control signal called ldquoblind graspingrdquo is defined and shown to be effective in stabilization of grasping without using the details of information of object shape and parameters or external sensing. The concept of stability of the closed-loop system under constraints is renewed in order to overcome the degrees-of-freedom redundancy problem. An extension of Dirichlet-Lagrange's stability theorem to a system of DOF-redundancy under constraints is presented by using a Morse-Lyapunov function.

In a musculoskeletal structure, the internal force among muscles plays an important role. Changing the internal force enables to control not only joint angles but also impedance, so that vertebrate animals can produce a motion according to a situation. Focusing on a musculoskeletal system with two links and six muscles, this paper investigate the effect of biarticular muscles when feedforward position control is inputted. This control gives the constant internal force balancing at desired posture as feedforward input, based on the EP hypothesis in physiology. From the result, we point out that the biarticular muscles can reduce the convergent time of the motion, and they also can stabilize the system.

A new robot hand dynamics model with rolling constraints and with a saddle joint at one finger is proposed, where two saddle-joint actuations are considered to be orderless. Spinning motion around the opposition axis connecting two center points of each finger-tip contact area with an object is faithfully treated, and a viscosity model for damping rotational motion of the object is proposed. A class of control signals without referring to object kinematics or using external sensing is proposed. Finally, numerical simulation results show the stability of motion of the overall closed-loop dynamics supplied with the proposed control input.

This paper proposes a novel control method for stable grasping using a multi-fingered hand-arm system with soft hemispherical finger tips. The proposed method is simple but easily achieves stable grasping of an arbitrary polyhedral object using an arbitrary number of fingers. Firstly, we formulate nonholonomic constraints between a multi-fingered hand-arm system and an object constrained by rolling contact with finger tips, and derive a condition for stable grasping by stability analysis. A new index for evaluating the possibility of stable grasping is proposed and efficient initial relative positions between finger tips and the object for realizing stable grasping are analyzed. The stability of the proposed system and the validity of the index are verified through numerical simulations.

This paper proposes a segmentation method of human manipulation task based on measurement of contact force imposed by a human hand on a grasped object. We define an index measure for segmenting a human manipulation task into primitives. The indices are calculated from the set of the contact forces measured at all the contact points during a manipulation task. Then, we apply the EM algorithm to the set of the indices in order to segment the manipulation task into primitives. These primitives are mapped onto the robotic hand to impose appropriate contact forces on a grasped object. In the experiments, manipulation tasks performed in daily human life have been successfully segmented.

This paper proposes a stable object grasping method to realize dynamic force/torque equilibrium by using a triple robotic fingers system with soft and deformable hemispherical fingertips. In the authors' previous works, ¿Blind Grasping¿ control scheme, which realizes stable object grasping without use of any external sensing such as vision, force, or tactile sensing in the case of using a pair of robot fingers, has been proposed. This control methodology is based on a unique configuration of human hand, called ¿Fingers-Thumb Opposability¿. In this paper, a ternary finger in addition to a pair of fingers is introduced not only to expand a stable region of grasping, but also to enhance dexterity and versatility of the multi-fingered robotic hand system. To this end, a ¿Blind Grasping¿ manner is modified in order to install it in the triple fingers system. First, dynamics of the triple robotic fingers system and a grasped object with considering rolling constraints is modeled, and a control input based on the blind grasping manner is designed. Next, the closed-loop dynamics is derived and a stability analysis is shown briefly. Finally, its usefulness is discussed through numerical simulation results.

Modeling of pinching an object with arbitrary shape by a pair of robot fingers with hemispherical ends in a horizontal plane is proposed in a mathematical and computational manner. Since the curvature of an object contour with an arbitrary curve is variable according to the change of the contact point between the object surface and the rigid finger tip, the arclength paremeter ¿s¿ explicitly appears in the overall fingers-object dynamics. It is shown that the overall fingers-object system should be accompanied with the first-order differential equation of the parameter ¿s¿ that includes the curvatures of both the object contour and finger-tip curve. A control input, which is of the same category as the control input called ¿blind grasping¿ appeared in our former papers, is utilized for the realization of stable grasp. The control input does neither need to use the kinematic information of the object nor use any external sensing. Finally, numerical simulations are carried out in order to confirm the effectiveness of our proposed model and control input.

Generally, point-to-point control for a completely restrained (CR) parallel-wire-driven system requires a balancing internal force to prevent slackening of wires, along with a feedback term based on some displacement sensor. This paper specifically describes CR systems' internal force properties, then presents the possibility of motion convergence at a desired position when the internal force balancing at a position is given as sensorless feedforward input. Subsequently, we use the property of internal force positively for sensorless position control. This positioning method is applicable for low-cost manipulation, which does not require high accuracy, and for emergency positioning of systems when sensors malfunction.

This paper proposes an iterative learning control method for simultaneous force/position tracking tasks by using a 5 D.O.F. robotic thumb under non-holonomic rolling constraints. In our previous works, "blind touching", which is defined as a point-to-point control scheme for the robot to realize a desired contact position and a contact force simultaneously without any external sensing, have proposed. In this paper, an iterative learning control manner to realize a desired continuous trajectory of the center of the contact point together with a desired contact force on the task plane is proposed. The usefulness of this learning control method is demonstrated by showing results of computer simulations.

This paper deals with dynamic object grasping and position control by using a triple-fingered robotic hand with hemispherical soft finger tips. In the authors’ previous works, a dynamic object grasping method by using a pair of robotic fingers, called “Blind Grasping”, which does not need to use any external sensing, was proposed. However, it is so far assumed that spinning motion around the opposite axis for the object does not occur during the overall movement, because there are only two opposite fingers and thereby the spinning is uncontrollable. By introducing a surplus finger in addition to a pair of robotic fingers, this spinning can be inhibited and it becomes controllable. In this paper, a triple-fingered robotic hand system with nonholonomic constraints is modeled, and a control input to realize stable grasping with object position control is proposed. Its usefulness and effectiveness are illustrated through some numerical simulation results.

Recently, active researches have been performed to increase a robot's intelligence so as to realize the dexterous tasks in complex environment such as in the street or homes. However, since the skillful human-like task ability is so difficult to be formulated for the robot, not only the analytical and theoretical control researches but also the direct human motion mimetic approach is necessary. In this paper, we propose that to realize the environmental interactive tasks, such as human care tasks, it is insufficient to replay the human motion along. We show a novel motion generation approach to integrate the cognitive information into the mimic of human motions so as to realize the final complex task by the robot.

Human can pinch or grasp and manipulate an object stably and dexterously. Accomplishment of such tasks is contributed from human hand's configuration, called "fingers-thumb opposability". This opposability of the thumb against other digits is specific and granted to only human among primates. When we use a cell phone, or change a TV's channel using a remote controller, we grasp it by a palm and digits other than the thumb, and push buttons using the thumb quickly, without looking the buttons. These kinds of thumb's movement seem to be one of the most intelligent movements in a human. Therefore, execution of such touching tasks without visual or tactile sensing is called in this paper "blind touching". The goal of this research is to realize humanlike "blind touching" by means of a 5 D.O.F. thumb robot model with soft and hemispherical finger-tip. To do this, we formulate a simultaneous contact position and touching force control by using 3-dimensional rolling contact with the task plane. First, dynamics of the 5 D.O.F. thumb robot model with hemispherical soft finger-tip under rolling constraints is derived. Then, a sensory-motor control law without vision, force or tactile sensing is proposed. Some numerical simulations show that the desired contact position and touching force can be attained by the proposed control scheme. A theoretical proof of convergence to the desired state is also presented.

In this paper, we study co-activation of digitorum muscles while perform stable pinching and posture regulation tasks of an object by using dual fingers. The fingers have 2 D.O.F. joints and are actuated by nonlinear redundant digitorum muscles to mimic human-like pinching movements. Firstly, we illustrate the kinematics and the dynamics of the overall system, which consider not only the fingers and an object, but also three muscles for each finger to actuate the finger links. Secondly, we consider nonlinear muscle property based on several physiological studies, and propose sensory-motor control rule to the muscles in order to realize stable pinching simultaneously with posture regulation by introducing internal force term induced by co-activation between flexor digitorums and extensor digitorums to modulate the damping factor in joint space. We verify our study by numerical simulations and conclude that this dual fingers system can realize human-like stable pinching and posture regulation

Our goal is to create advanced engineering systems such as a soft human interactive robot. The robot developed here is named RI-MAN. RI-MAN exhibits the skill and ability to realize human care and welfare tasks. RI-MAN can search out a specific person in real time by fuing audio and visual information, and understand human speech based on a sound recognition function. In addition, RI-MAN's body is coverd with soft touch sensors, and RI-MAN can react to the amplitude and location of external forces. Using all these sensor functions, RI-MAN can successfully follow human commands and hold up a dummy of the same size as an adult human. RI-MAN will become an invaluable partner robot.

This paper focuses on a sensory-motor control mechanism in human reaching movements from the perspective of robotics. By formulating a musculo-skeletal redundant system which takes into account a nonlinear muscle property and performing numerical simulations, we suggest that the human-like reaching movements can be realized by using only simple task-space feedback scheme together with the internal force effect coming from nonlinear property of muscles without any complex mathematical computation such as an inverse dynamics or some optimal trajectory derivation. Firstly, we introduce both kinematics and dynamics of a three-link serial manipulator with six monoarticular muscles and three biarticular muscles model whose movements are limited within a horizontal plane. Secondly, the nonlinear muscle property coming from a physiological study based on Hill's muscle model, is taken into consideration. This nonlinearity makes it possible to modulate the damping effect in joint-space by considering the internal force generated by the redundant muscles. By utilizing this feature, the end-point converges to the desired point using only simple task-space feedback control scheme, even thought the system owns both the joint and muscle redundancies. Finally, we illustrate numerical simulations to show the effectiveness of the control scheme, and suggest one of the direction to study brain-motor control mechanism of human movements

It is well-known that a human musculo-skeletal body is redundant in terms of both kinematics and dynamics. The former means that the degree of freedom in joint space is larger than that in task space, and the latter means that a joint is driven by a number of muscles. All human skillful movements can be performed by using both redundancies. However, these redundancies induce the underlying ill-posedness problem that each joint angle and muscle’s output forces cannot be uniquely determined. These ill-posedness problems are known as “Bernstein’s problem” and are important to understand how human multi-joint movements are produced. In this study, we address the latter redundancy problem on how muscle’s output forces can be determined from the viewpoint of robotics. In this paper, we consider a reaching movement by means of a two-link planar arm with six muscles and show that both damping and elastic properties coming from nonlinear dynamics of the muscles play a crucial role. By using a simple task space feedback control input together with an additional term to control the internal force to regulate damping and elasticity in joint space, we show some simulation results which exhibit human-like quasi-straight line movement.

In this paper, we study the sensory motor control mechanism in human reaching movements by considering the redundant muscle dynamics. We first formulate the kinematics and dynamics of a two-link arm model with six muscles, and introduce the nonlinear muscle dynamics based on the biological understanding. Secondly, we show the stability of the system by using intrinsic muscle characteristics and La Salle's invariance theorem. From this result and the numerical simulations, we propose that the reaching movement can be regulated by the internal forces of the redundant muscles, in detail the muscle's internal forces can be used to control the damping of the joints. In addition, human can compensate the gravity by using antigravity muscles. To realize this effect in the arm, we propose the gravity compensation method at the muscle input level from the viewpoint of robotics. We present the result of numerical simulation to verify the usefulness of this compensation method.

This paper studies modeling and bio-mimetic control of a 3D 8-dof whole arm cooperative manipulation system using sensitive skin. A sphere is considered here as a manipulated object. The control law is designed based on integration of voluntary and reflex movements considering the system's redundancy. The voluntary task for holding the object is realized by impedance control at the four contact points with the object using the contact force information from the sensitive skin. The reflection on the other hand is introduced as a regulation problem of the direction between the points of end-effectors and elbows. The solution for the redundant control is formulated and derived from the optimization point of view. The validity of the proposed method is investigated by numerical simulations.

This paper is concerned with a stability theory of motion governed by Lagrange's equation for a pair of multi-degrees of freedom robot fingers with hemispherical finger ends grasping a rigid object under rolling contact constraints. When a pair of two DOF. fingers is used and motion of the overall fingers-object system is confined to a plane, it is shown that the total degree of freedom of the fingers-object system is redundant for realization of stable grasping though there arise four algebraic constraints. To resolve the redundancy problem without introducing extra performance specifications, a concept of stability of motion starting from a higher dimensional manifold to a lower-dimensional manifold expressing a set of states of stable grasp with prescribed contact force is introduced and thereby it is proved in a rigorous way that stable grasp in a dynamic sense is realized by a sensory feedback constructed on the basis of measurement data of finger joint angles and the rotational angle of the object. Further, it is shown that there exists an additional sensory feedback that realizes not only stable grasp but also orientation control of the object concurrently. These results can be extended to other two cases that: 1) motion of the overall system is confined to a vertical plane and therefore it is affected directly by the gravity; and 2) the object has non-parallel but flat surfaces.

This paper proposes a pair of single or multi-DOF robot fingers with soft and deformable tips that can pinch an object stably in a dynamic sense with the aid of real-time sensory feedback. To realize dynamic stable pinching, a practical method of using optical devices is proposed for measuring both the maximum displacement of finger-tip deformation and the relative angle between the object surface and each of finger links. It is shown that the overall closed-loop system of a pair of two single-DOF fingers with soft tips with real-time sensory feedback of the difference between the centers of two area-contacts at both sides of the object becomes asymptotically stable. This means that the pair achieves dynamic stable grasping (pinching). In the case of a pair of 1-DOF and 2-DOF fingers with soft tips, it is shown that the proposed method of closed-loop feedback of the difference between the centers of two area-contacts and the rotational angle of the object can establish not only dynamic stable grasping but also regulation of the posture of the object.